Time series data is of growing importance in a wide range of domains and applications such as predictive maintenance, database applications, and so forth. Consequently, much research has been done on time series data mining, focusing on forecasting, indexing, clustering, classification, and anomaly detection. Time series data is generally collected in the form of a sequence of real numbers, where each number is a value at a time point. Time series data can be of high dimension and may exhibit a high degree of feature correlation. Such characteristics make time series data mining tasks difficult, which has been described as the dimensionality curse problem. Handling high dimension time series data can be expensive with respect to processing and storage costs. Thus, applying currently used techniques to handle high dimension time series data can degrade the performance of time series data mining algorithms, while also degrading the performance of computing systems on which the analysis is performed.